Discussion About the Application of Continuation Task in National Matriculation English Test: Take the National Matriculation English Test in 2020 in Zhejiang and Shandong Province Writing as An Example
Bibliographic record
Abstract
The continuation task is a new form of reading-to-write integrated task in which test-takers read an incomplete story and then write the continuation and ending of the story. It has been increasingly used in writing assessment in China. It can test students’ comprehensive ability of reading and writing. This type of writing task first appeared in 2016 National Matriculation English Test (Zhejiang Province version). In this paper, the author reviews the research and summarizes that the continuation task is more suitable for the language ability testing of Chinese college entrance examination candidates. What`s more, after making a content validity analysis of the NMET (Zhejiang) and NMET (Shandong) in 2020, the author found that this task has the characteristics of moderate openness and helpful to the examination of creative thinking ability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".